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WO2012137814A1 - Contrôleur et procédé de commande - Google Patents

Contrôleur et procédé de commande Download PDF

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Publication number
WO2012137814A1
WO2012137814A1 PCT/JP2012/059185 JP2012059185W WO2012137814A1 WO 2012137814 A1 WO2012137814 A1 WO 2012137814A1 JP 2012059185 W JP2012059185 W JP 2012059185W WO 2012137814 A1 WO2012137814 A1 WO 2012137814A1
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Prior art keywords
prediction
prediction information
information
power consumption
information parameter
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PCT/JP2012/059185
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English (en)
Japanese (ja)
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嘉浩 北浦
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シャープ株式会社
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Definitions

  • the present invention relates to a controller arranged in a house or the like and connected to an electric device in the house and a control method.
  • the controller can control a heat pump water heater, an air conditioner, a refrigerator, lighting, a liquid crystal television, and the like, and can display the status of these devices.
  • a controller that can appropriately control other devices based on the weather or the like has been proposed.
  • the controller predicts the future power generation energy by the solar power generation device in the home and the energy consumption consumed by the energy consuming device, etc., and based on the prediction, formulates the operation schedule of the device and controls the device.
  • the controller realizes device control that achieves both energy saving and comfort for the resident. However, if the prediction is wrong, the resident may be uncomfortable. Therefore, the device control is a very important technique that requires the accuracy of the prediction.
  • energy consumption correlates with information such as weather, occupants, and device status (for example, depending on the device status (number of operating energy consumption devices) Etc.)
  • the accuracy of prediction can be improved by performing prediction using a plurality of pieces of information instead of a single piece of information.
  • Patent Document 1 discloses a power network management system and a power network management method.
  • the control device of each natural energy power generation system predicts the power generation amount on the scheduled power generation date based on the weather information on the planned power generation date according to the weather forecast or the current season and the predetermined power generation amount information
  • the power network management device predicts the power consumption amount of the load on the scheduled power generation date based on the weather information or the current season or the date or day of the scheduled power generation date and the predetermined power consumption information.
  • the amount of power supplied to each natural energy power generation system is determined so that the total amount of power supplied to the power network on the scheduled power generation date is constant based on the amount of power and the amount of power consumption.
  • Patent Document 2 discloses a power fluctuation prediction system. According to Patent Literature 2, the power fluctuation prediction system corrects similar power change curve information, which is past power change curve information, with external environment data to create equipment group power change curve information creation unit.
  • an in-system grid power change curve information creation unit that creates a plurality of first in-house system power change curve information by adding together the equipment group power change curve information of different equipment groups, and a plurality of first in-house systems
  • a first increase / decrease-side power change curve information creation unit that creates first increase-side power change curve information and first decrease-side power change curve information based on the average value and standard deviation value of power in the power change curve information
  • a power fluctuation information creating unit that calculates at least one of a maximum change width and a maximum change rate of the power fluctuation calculated from the first increase power change curve information and the first decrease power change curve information.
  • Patent Document 3 discloses an energy consumption prediction method and an energy consumption prediction device.
  • a method and apparatus for predicting energy consumption includes personal information including at least gender, generation, and occupation type of a consumer, and device information including at least the type and number of facility devices that consume energy used by the consumer. And an equipment operation schedule and equipment energy consumption database. Enter personal information including at least gender, generation and occupation of the people living in the building and equipment information including at least the type and number of equipment that consumes the energy used by the consumers.
  • the energy consumption is predicted by calculating the operation schedule for each created time division for each equipment and the energy consumption for each equipment set in advance.
  • Patent Document 4 discloses an energy consumption calculation system and an energy consumption calculation method.
  • an energy consumption system that calculates information related to energy consumption of a device includes a consumption model database that stores a consumption model indicating an energy fee of a predetermined device in a predetermined space, spatial information, and the spatial information.
  • a usage model reception unit that receives an input of a usage model associated with a usage time of a space, a setting information reception unit that receives an input of installation information in which device information and spatial information are associated, a received usage model, and An operation status estimation unit that estimates the operation status of the device based on the installation information, a calculation unit that calculates an energy fee in the operation status based on the estimated operation status and consumption model, and outputs the energy fee And an output unit.
  • JP 2004-072900 A JP 2009-055713 A JP 2003-281223 A JP 2008-250542 A
  • the present invention has been made to solve such a problem.
  • the objective in one situation is providing the controller for controlling an electric equipment more appropriately.
  • the objective in the other situation is to provide the control method of an electric equipment.
  • a controller for controlling a plurality of electric devices connected via a network is provided.
  • the controller is based on a memory for storing a database including power consumption amounts of a plurality of electrical devices in association with each of a plurality of prediction information, and a predicted power consumption amount calculated using reliability related to the prediction information.
  • a device control unit for controlling a plurality of electric devices is provided.
  • the controller further includes a prediction information parameter generation unit configured to generate a plurality of prediction information parameters related to future power consumption consumed by the network-connected electrical device.
  • each prediction information parameter and the power consumption corresponding to the prediction information parameter are accumulated for each time segment.
  • the controller includes at least one selected from a plurality of prediction information parameters according to a reliability generation unit configured to generate a reliability indicating the certainty of each prediction information parameter and the reliability corresponding to each prediction information parameter.
  • a power consumption amount prediction unit configured to use the power consumption amount in the database as a predicted value using one prediction information parameter.
  • the reliability generation unit is configured to calculate the reliability from each prediction information parameter at the start time of the prediction target period and a degree of change in each past prediction information parameter accumulated in the database. .
  • the power consumption amount prediction unit stores information on past power consumption amounts that coincide with the prediction information parameter excluding the prediction information parameter corresponding to at least the minimum value among the reliability corresponding to each prediction information parameter. And the extracted information is used as a predicted value.
  • the power consumption amount prediction unit excludes the prediction information parameter corresponding to at least the minimum value when at least one of the reliability levels corresponding to the prediction information parameters is smaller than a predetermined value.
  • Information regarding past power consumption that matches the parameters is extracted from the database, and the extracted information is used as a predicted value.
  • a control method for controlling a plurality of electric devices connected via a network using a controller having a processor controls the plurality of electrical devices based on the predicted power consumption calculated using the reliability regarding the plurality of pieces of prediction information associated with the power consumption of each of the plurality of electrical devices. Including that.
  • the method generates the reliability indicating the certainty of each prediction information parameter based on each prediction information parameter at the start time of the prediction target period and each past prediction information parameter, and each prediction information parameter And predicting the power consumption based on at least one prediction information parameter selected from a plurality of prediction information parameters according to the reliability corresponding to.
  • the method includes calculating the reliability from each prediction information parameter at the start time of the prediction target period and the degree of change in each past prediction information parameter.
  • the method extracts information on past power consumption that matches the prediction information parameter excluding the prediction information parameter corresponding to at least the minimum value among the reliability corresponding to each prediction information parameter, and predicts Including value.
  • the method excludes the prediction information parameter corresponding to at least the minimum value when the extracted information is smaller than at least one of the reliability corresponding to each prediction information parameter. It includes extracting information on the past power consumption that matches the prediction information parameter, and using the extracted information as a prediction value.
  • a controller and an information processing method that can more appropriately control an electric device are provided.
  • FIG. It is an image figure which shows the whole structure of the network system 1 which concerns on this Embodiment. It is an image figure of the database stored in the memory 101 which concerns on this Embodiment. It is a block diagram showing the hardware constitutions of the controller 100 which concerns on this Embodiment. 2 is a block diagram showing a functional configuration of a controller 100.
  • FIG. It is an image figure which shows how to obtain
  • It is an image figure which shows the correspondence data of the indoor number of persons and a parameter in the case of L 10. It is a flowchart which shows the calculation method of the predicted power consumption in this embodiment. It is an image figure which shows the relationship between a prediction information parameter and power consumption. It is an example which showed the relationship between the power consumption for every time division, and a prediction information parameter. It is an example which showed the relationship of the reliability of each prediction information parameter for every time division. It is a block diagram showing the hardware constitutions of the electric equipment 200 which concerns on this Embodiment.
  • FIG. 1 is an image diagram showing an overall configuration of a network system 1 according to the present embodiment.
  • network system 1 is installed in, for example, a house or an office.
  • the network system 1 includes a controller 100.
  • Network system 1 includes a heat pump water heater 200A, an air conditioner 200B, a refrigerator 200C, an illumination 200D, and a liquid crystal television 200E controlled by the controller 100.
  • the heat pump water heater 200A, the air conditioner 200B, the refrigerator 200C, the lighting 200D, and the liquid crystal television 200E are collectively referred to as home appliances (electric equipment 200).
  • the network system 1 includes a sensing device 200S (human sensor 200G, temperature / humidity sensor 200H, electric power sensor 200I, anemometer 200J, anemometer 200K, an anemometer 200L) controlled by the controller 100, and a smart meter 200R. .
  • a sensing device 200S human sensor 200G, temperature / humidity sensor 200H, electric power sensor 200I, anemometer 200J, anemometer 200K, an anemometer 200L
  • a smart meter 200R controlled by the controller 100
  • a smart meter 200R controlled by the controller 100
  • the network system 1 includes a power conditioner 200X controlled by the controller 100, a solar battery 200Y and a storage battery 200Z controlled by the controller 100 via the power conditioner 200X.
  • the storage battery 200Z may be provided in a house or the like, or may be a battery for an automobile.
  • the controller 100 performs data communication with the above apparatus via a wired or wireless network.
  • a wireless LAN for example, a wireless LAN, ZigBee (registered trademark), Bluetooth (registered trademark), wired LAN (Local Area Network), or PLC (Power Line Communications) is used.
  • the controller 100 may be portable, may be detachable from a base placed on a table, or may be fixed to a wall of a room.
  • the network system 1 receives information on factors that affect the power consumption of the electrical device 200, such as weather information, via the Internet.
  • the controller 100 controls the power conditioner 200X to acquire power from the system or the solar cell 200Y or the storage battery 200Z, or the electric device 200 or the system or the solar cell 200Y. Or electric power is supplied to the storage battery 200Z.
  • FIG. 2 is an image diagram of a database stored in the memory 101 according to the present embodiment.
  • controller 100 has a database in memory 101.
  • the database stored in the memory 101 is generated from a meteorological information parameter generation unit 110A (see FIG. 4), a personal information parameter generation unit 110B, a device information parameter generation unit 110C, an equipment information parameter generation unit 110D, and the like, which will be described later.
  • a meteorological information parameter generation unit 110A see FIG. 4
  • a personal information parameter generation unit 110B a personal information parameter generation unit 110B
  • a device information parameter generation unit 110C an equipment information parameter generation unit 110D, and the like, which will be described later.
  • Each value of the prediction information parameter, the power generation amount measured by the power generation amount measurement unit (power conditioner) 110Y, and the power consumption amount measured by the power consumption amount measurement unit 200U is stored.
  • the power generation and power consumption corresponding to the prediction information parameter are accumulated, and the time, power generation and power consumption corresponding to the prediction information parameter of the input data are stored. Each value is updated.
  • the controller 100 overwrites or averages the values to generate the power generation amount and the power consumption amount. Update each value.
  • controller 100 calculates a predicted value of power consumption by using weather information, personal information, device information, and facility information.
  • the controller 100 may use only a part of the information described above, or use information for specifying an environment around the network system 1 that affects other power consumption. May be.
  • the weather information includes information indicating weather (clear / rainy / cloudy), information indicating temperature (for example, a degree Celsius, b degree Fahrenheit), and / or information indicating humidity (c%), etc. including.
  • the controller 100 decreases the power consumption allocated to the refrigerator 200C and increases the power consumption allocated to the heat pump water heater 200A.
  • the controller 100 increases the power consumption allocated to the illumination 200D and decreases the power consumption allocated to the backlight of the liquid crystal television 200E.
  • Personal information includes information indicating the number (x) of people staying in a house or office, information indicating the gender of each person, and / or information indicating the age of each person.
  • the device information includes information indicating the number (N) of electric devices 200 installed in a house or office (connected to the controller 100), and the state (normal / abnormal / operating / stopped) of each electric device 200. ), Information indicating the operation mode, and / or information indicating the input command.
  • the facility information includes information (room, shape, etc.) relating to a house or an office room, information relating to a door (position and opening / closing state), information relating to a window (position and opening / closing state), and the like.
  • the controller 100 generates a prediction information parameter from the sensing device 200S (human sensor 200G, temperature / humidity sensor 200H, power sensor 200I, anemometer 200J, anemometer 200K, solar radiation meter 200L) or the operation unit of the controller 100.
  • Weather information, personal information, device information, and facility information are acquired as basic prediction information used for the operation.
  • the controller 100 accumulates weather information, personal information, device information, and facility information in a database stored in the memory 101. That is, the database stores time-series data regarding weather information, personal information, device information, and facility information.
  • the controller 100 calculates the reliability regarding the weather information based on the time series data of the weather information in the database stored in the memory 101. The smaller the variation in weather information, the higher the reliability related to weather information.
  • the controller 100 calculates the reliability related to the personal information based on the time-series data of the personal information stored in the memory 101. The smaller the fluctuation of the personal information, the higher the reliability related to the personal information.
  • the controller 100 calculates the reliability related to the device information based on the time series data of the device information in the database stored in the memory 101. The smaller the fluctuation of the device information, the higher the reliability related to the device information.
  • the controller 100 calculates the reliability related to the facility information based on the time-series data of the facility information in the database stored in the memory 101. The smaller the fluctuation of the facility information, the higher the reliability related to the facility information.
  • FIG. 4 is a block diagram illustrating a functional configuration of the controller 100.
  • the controller includes a prediction information parameter generation unit 110F, a power generation amount prediction unit 110E, a power consumption amount prediction unit 110H, a device control unit 110J, a reliability generation unit 110G, and a memory 101 that stores a database.
  • the prediction information parameter generation unit 110F, the power generation amount prediction unit 110E, the power consumption amount prediction unit 110H, the device control unit 110J, and the reliability generation unit 110G are calculated by the CPU 110 based on a program stored in the memory 101. This is realized.
  • the controller 100 is connected to a power conditioner 200X.
  • a renewable energy generation unit 400Y is connected to the power conditioner 200X.
  • the controller 100 measures the amount of power generated by the network, the sensing device 200S, and the prediction information output from the camera device 200T, and the amount of power generated by the renewable energy generator 400Y corresponding to the solar cell 200Y and other devices.
  • Power input information output from the power unit (power conditioner 200X) and power consumption information output from the power consumption measuring unit 200U that measures the amount of power consumed by the electric device 200 are received and connected to the network
  • Device control information is output to the electric device 200.
  • the device control information indicates ON / OFF control of a function associated with the device and parameters (for example, temperature information and strength information) indicating the degree of the function.
  • the sensing device 200S is a device that is installed indoors or outdoors and detects various phenomena or events.
  • the sensing device 200S is, for example, a temperature sensor, an illuminance sensor, a human sensor, or the like, but is not limited to these devices.
  • the camera device 200T is a device that captures still images and images installed indoors or outdoors.
  • the camera device 200T is, for example, a surveillance camera or a WEB camera, but is not limited to these devices. Further, the camera device 200T may be equipped with a recognition and authentication function and have the same role as the sensing device 200S.
  • the renewable energy generator 400Y is a device that is installed outdoors and generates electricity using a phenomenon that occurs repeatedly in the natural environment.
  • the aspect of electric power generation is sunlight, wind power, solar thermal power generation, etc.
  • the renewable energy generation part 400Y is not limited to the apparatus for implement
  • the power generation amount measurement unit 400X measures the renewable energy (power generation amount) output from the renewable energy generation unit.
  • the power generation amount measurement unit 400X is realized as the power conditioner 200X that measures the cumulative power generation amount generated within a predetermined time segment, but is not limited to this device.
  • the electrical device 200 is an electrical device having a network connection function with the controller.
  • the electric device 200 is, for example, an air conditioner, a television, a refrigerator, a lighting device, or the like, but is not limited to these devices.
  • the power consumption measuring unit 200U is a device that measures the amount of power consumed by an electrical device.
  • the power consumption measuring unit 200U is a device that measures the accumulated power consumed within a predetermined time interval, but is not limited to this device.
  • the power consumption measuring unit 200U may be mounted on each electric device 200, may be mounted on the controller 100, or may be provided separately from the electric device 200 and the controller 100.
  • the controller 100 processes information for each time segment (30 minutes, 1 hour, 1 day, etc.). For example, when the time division is 30 minutes, the output from the controller is the device control information 30 minutes after the current time.
  • the prediction information parameter generation unit 110F acquires basic information regarding prediction from the outside, converts the basic information regarding the prediction into a prediction information parameter that can be used by the power generation amount prediction unit 110E and the power consumption amount prediction unit 110H, and outputs the prediction information parameter.
  • the prediction information parameter generation unit 110F includes M types of information parameter generation units.
  • the prediction information parameter generation unit 110F includes a weather information parameter generation unit 110A, a facility information parameter generation unit 110D, a device information parameter generation unit 110C, and a personal information parameter generation unit 110B.
  • the prediction information parameter generation unit 110F is assumed to be composed of four types of information parameter generation units, but is not limited to four types. Further, an information parameter generation unit related to information other than the prediction information parameter generation unit 110F may be used.
  • the meteorological information parameter generation unit 110A inputs weather information from an external network and the sensing device 200S, and generates a weather information parameter.
  • the weather information is information on outdoor temperature, humidity, and illuminance, for example.
  • the facility information parameter generation unit 110D receives the facility information input from the sensing device 200S and outputs the facility information parameter.
  • the facility information is, for example, information related to door opening / closing status, indoor illuminance, humidity and illuminance.
  • the device information parameter generation unit 110C receives the input of the device information output from the device control unit 110J and generates a facility information parameter.
  • the device information relates to ON / OFF information of functions associated with each electrical device and information indicating the degree of function.
  • the personal information parameter generation unit 110B receives input of personal information from the sensing device 200S and the camera device 200T, and generates a personal information parameter.
  • the personal information is information related to information (for example, the number of people, sex, and age group) of people who are indoors.
  • the prediction information parameter generation unit 110F performs a process of converting into parameters that can be used by the power generation amount prediction unit 110E or the power consumption amount prediction unit 110H in order to treat information acquired from the outside equally.
  • the prediction information parameter generation unit 110F converts information input based on a predetermined table into L-stage parameters. Each information parameter generation unit holds a plurality of table tables, selects a table table corresponding to information to be handled, and performs parameter conversion.
  • the correspondence data between the external temperature and the parameter, the correspondence data between the number of indoor open doors and the parameter, the number of operating electric devices And the correspondence data between the parameters, and the correspondence data between the number of people in the room and the parameters are stored.
  • each information is divided by the same section width for each parameter.
  • the classification of each information is not limited to this method, and is divided by a different section width for each parameter. Also good.
  • the database stored in the memory 101 is measured by the prediction information parameter generated by the prediction information parameter generation unit 110F, the power generation amount measured by the power generation amount measurement unit (power conditioner 200X), and the power consumption amount measurement unit 200U. Each value of power consumption is stored. As shown in FIG. 2, the database stores the power generation amount and the consumption amount corresponding to the prediction information parameter.
  • the controller 100 updates values of time, power generation amount, and power consumption amount corresponding to the prediction information parameter of the input data.
  • past data past data accumulated in the database stored in the memory 101 will be collectively referred to as past data below.
  • the power generation amount prediction unit 110E predicts the power generation amount in the prediction target period using each prediction information parameter at the current time and past data.
  • the prediction target period is a period from the current time to the end of a predetermined time segment. For example, when the current time is 16:00 and the time segment is 30 minutes, the prediction target period indicates a section from 16:00 to 16:30. Note that the amount of power generation in the forecast period is unknown because it is a future event. Thus, the current time means the start time of the prediction target period.
  • the device control unit 110J receives input of each value of the predicted power generation amount predicted by the power generation amount prediction unit 110E and the predicted power consumption amount predicted by the power consumption amount prediction unit 110H. Create an operation schedule.
  • the operation schedule realizes both energy saving and comfort for the resident, and the device control unit 110J outputs information indicating an operation based on the operation schedule to the electric device 200.
  • the information includes ON / OFF information of the air conditioner power supply, an operation mode (cooling, heating, air blowing, etc.), temperature information, and wind direction information.
  • the power consumption prediction unit 110H includes each prediction information parameter at the current time (start time of the prediction target period), reliability corresponding to each prediction information parameter output from the reliability generation unit 110G described later, and past Using data, the power consumption amount in the prediction target period is predicted. Note that the power consumption in the prediction target period is unknown because it is a future event.
  • FIG. 11 is an image diagram showing the relationship between the prediction information parameter and the power consumption.
  • the horizontal axis is the prediction information parameter
  • the vertical axis is the power consumption.
  • the straight line on FIG. 11 is an approximate line, and the closer to the approximate line, the higher the correlation.
  • the power consumption varies depending on the number of indoor electrical devices in operation.
  • the prediction information parameter is large
  • the amount of power consumption tends to increase.
  • the prediction information parameter is small
  • the power consumption is in a downward trend.
  • the correlation is further enhanced by using a plurality of prediction information parameters.
  • FIG. 12 is an example showing the relationship between the power consumption for each time segment and the prediction information parameter.
  • prediction is performed using the current time T to time T + 1 as a prediction target period.
  • the processor using the conventional method first searches for prediction information parameters that match the parameters from past data (data before the current time T). As a result of the search, since the prediction information parameter coincides with the time T-5, the processor sets the power consumption W2 at the time T-5 as the predicted value of the power consumption in the time segment starting from the current time T. In the example illustrated in FIG. 6, the predicted value of the power consumption is “586”.
  • the actual power consumption and the predicted power consumption are almost the same. That is, when the prediction information parameters match at different times, the power consumption amounts in the time segments starting from that time also match. For example, if the power consumption in the time interval starting from time T-1 is unknown, the predicted power consumption is W1 and the actual power consumption is also W1, so that it is understood that the prediction is performed accurately.
  • an index for quantitatively evaluating each prediction information parameter is defined as the reliability.
  • the reliability is given to each prediction information parameter, and is calculated as needed at each time.
  • the concept of reliability is not applied.
  • the reliability for each prediction information parameter can be defined as a maximum value. That is, since all the prediction information parameters are determined to be reliable, the information used for prediction is not selected, and all the prediction information parameters are used for prediction.
  • the controller 100 determines whether to use the prediction information parameter for prediction by comparing the reliability corresponding to each prediction information parameter with a predetermined value. If the reliability is greater than a predetermined value, the controller 100 determines that the reliability is high and uses the corresponding prediction information parameter in the prediction. On the other hand, if the reliability is smaller than a predetermined value, the controller 100 determines that the reliability is low, and excludes the corresponding prediction information parameter from the prediction.
  • the number of prediction information parameters to be selected may be one, but a plurality is preferable.
  • FIG. 13 shows changes in reliability for each time segment when there are four types of prediction information parameters (weather information parameter, facility information parameter, device information parameter, and personal information parameter).
  • the predetermined threshold value is 50, and the number of prediction information parameters to be excluded is 1.
  • the personal information parameter is excluded, and only the weather information parameter, facility information parameter, and device information parameter are selected and used for prediction.
  • the reliability of the device information parameter and the personal information parameter is below the threshold value 50.
  • the device information parameter is excluded from time 9 to 12 and only the weather information parameter, the facility information parameter, and the personal information parameter are excluded. Is selected and used for prediction.
  • personal information parameters are excluded, and only weather information parameters, facility information parameters, and device information parameters are selected and used for prediction.
  • the personal information parameter is excluded, and only the weather information parameter, facility information parameter, and device information parameter are selected and used for prediction.
  • the equipment information parameter is excluded, and only the weather information parameter, the equipment information parameter, and the personal information parameter are selected and used for prediction.
  • the controller 100 selects prediction information to be used for prediction based on the magnitude relationship of reliability at each time.
  • the reliability generation unit 110G calculates the reliability corresponding to each prediction information parameter in the prediction target period using the prediction information parameter stored in the database stored in the memory 101 and each value of the power consumption. .
  • the reliability in this embodiment is calculated based on the absolute value of the difference between the sections in a certain section.
  • RX (T) A ⁇ P / ⁇ (1 + (
  • )) (T T ⁇ TP)
  • t is a time variable
  • T is the current time
  • X (t) is the prediction information parameter of the prediction information X at time t
  • P is the number of time segments in the section
  • RX (t) is the reliability of the prediction information X at time t.
  • A indicates a positive constant.
  • FIG. 10 is a flowchart illustrating a method for calculating the predicted power consumption in the present embodiment.
  • Step S2 The power consumption amount prediction unit 110H compares the reliability corresponding to the M prediction information parameters with the reliability threshold value.
  • Th Reliability threshold
  • the power consumption prediction unit 110H determines that the M prediction information parameters are larger than the reliability threshold Th, that is, all the prediction information parameters are reliable, it is not necessary to select the prediction information parameters, Transition to step S4.
  • the power consumption prediction unit 110H determines that at least one of the reliability of the M prediction information parameters is smaller than the reliability threshold Th, that is, some of the prediction information parameters are not reliable, the prediction information parameter Since it is necessary to select, the process transitions to step S3.
  • Step S3 Since the power consumption amount prediction unit 110H determines that it is necessary to select a prediction information parameter in Step S2, the prediction information parameter is selected.
  • the power consumption amount prediction unit 110H excludes only the prediction information parameter having the minimum value from the M reliability levels.
  • the power consumption amount prediction unit 110H excludes two prediction information parameters having small values from among the M reliability levels. That is, the power consumption prediction unit 110H excludes S prediction information parameters with low reliability from the M prediction information parameters, and uses only the MS prediction information parameters for prediction.
  • Step S4 Since the power consumption amount prediction unit 110H determines that it is not necessary to select the prediction information parameter in Step S2, the prediction information parameter is not selected. That is, the power consumption prediction unit 110H uses all M prediction information parameters for prediction.
  • Step S5 The power consumption prediction unit 110H extracts the power consumption of the time segment starting from the time that coincides with the MS prediction information parameters in the MS past data selected in Step S3. .
  • Step S6 The power consumption amount prediction unit 110H extracts the power consumption amount of the time section starting from the time that coincides with the M prediction information parameters in the past data.
  • Step S7 The power consumption prediction unit 110H outputs the power consumption extracted in step S5 or step S6 as the predicted power consumption.
  • the controller 100 calculates a predicted value of the power consumption amount for each electrical device based only on the power consumption amount corresponding to information whose reliability is equal to or higher than a predetermined value.
  • the controller 100 includes a predicted value of power consumption of a plurality of electrical devices, an upper limit value of power consumption related to the entire house or office set via an operation unit, and a predicted value of power generation of the solar cell 200Y. Based on this, the operation of each electrical device is controlled by calculating the amount of power consumption allocated to each electrical device.
  • the controller 100 according to the present embodiment calculates the predicted amount of power consumption in consideration of the reliability regarding weather information, personal information, device information, and facility information. That is, information with low reliability and information with high reliability can be appropriately used for power consumption prediction according to the degree of external environmental change and the like. As a result, the controller 100 according to the present embodiment can control the electrical device more appropriately than the conventional one.
  • FIG. 3 is a block diagram showing a hardware configuration of controller 100 according to the present embodiment.
  • the controller 100 includes a memory 101 for storing a database, a display 102, a tablet 103, a button 104, a communication interface 105, a speaker 107, a clock 108, and a CPU (Central Processing Unit) 110 that is a processor.
  • the database is stored in a memory 101 realized by various memories, for example, RAM (Random Access Memory), ROM (Read-Only Memory), a hard disk, and the like.
  • the database is used via a read interface, USB (Universal Serial Bus) memory, CD-ROM (Compact Disc-Read Only Memory), DVD-ROM (Digital Versatile Disk-Read Only Memory), memory Card, FD (Flexible Disk), hard disk, magnetic tape, cassette tape, MO (Magnetic Optical Disc), MD (Mini Disc), IC (Integrated Circuit) card (excluding memory card), optical card, mask ROM, EPROM, It can also be stored in a memory realized by a medium for storing a program in a nonvolatile manner such as an EEPROM (Electronically Erasable Programmable Read-Only Memory).
  • EEPROM Electrically Erasable Programmable Read-Only Memory
  • the database stored in the memory 101 stores a control program executed by the CPU 110 and various data. More specifically, as described above, the database stored in the memory 101 associates weather information, a predicted value of power consumption for each electrical device 200 for a predetermined period ahead of a predetermined time, and the amount of power generated by the solar cell 200Y. Store.
  • the database stores the personal information, the predicted value of the power consumption for each electric device 200 in a predetermined period ahead of the predetermined time, and the power generation amount by the solar battery 200Y in association with each other.
  • the database stores the device information, the predicted value of the power consumption amount for each electric device 200 in a predetermined period ahead of the predetermined time, and the power generation amount by the solar battery 200Y in association with each other.
  • the database stores the facility information, the predicted value of the power consumption for each electric device 200 in a predetermined period ahead of the predetermined time, and the power generation amount by the solar battery 200Y in association with each other.
  • the weather information includes information indicating weather (clear / rainy / cloudy), information indicating temperature (a degree Celsius or b degree Fahrenheit), and / or humidity (c%). It includes information indicating.
  • the personal information includes information indicating the number (x) of people staying in a house or office, information indicating the gender of each person, and / or information indicating the age of each person.
  • the device information includes information indicating the number (N) of electric devices 200 installed in a house or office (connected to the controller 100), and the state (normal / abnormal / operating / stopped) of each electric device 200. ), Information indicating the operation mode, and / or information indicating the input command.
  • the facility information includes information (room, shape, etc.) relating to a house or an office room, information relating to a door (position and opening / closing state), information relating to a window (position and opening / closing state), and the like.
  • the display 102 is controlled by the CPU 110 to display the state of the electric device and the power conditioner 200X.
  • the tablet 103 detects a touch operation with a user's finger and inputs touch coordinates or the like to the CPU 110.
  • the CPU 110 receives a command from the user via the tablet 103.
  • the tablet 103 is provided on the surface of the display 102. That is, in the present embodiment, display 102 and tablet 103 constitute touch panel 106. However, the controller 100 may not have the tablet 103.
  • the button 104 is disposed on the surface of the controller 100.
  • a plurality of buttons such as a determination key, a direction key, and a numeric keypad may be arranged on the controller 100.
  • the button 104 receives a command from the user.
  • the button 104 inputs a command from the user to the CPU 110.
  • the communication interface 105 transmits / receives data to / from an electric device via a network by being controlled by the CPU 110. As described above, the communication interface 105 transmits and receives data to and from an electrical device by using, for example, a wireless LAN, ZigBee (registered trademark), Bluetooth (registered trademark), wired LAN, or PLC.
  • a wireless LAN ZigBee (registered trademark), Bluetooth (registered trademark), wired LAN, or PLC.
  • Speaker 107 outputs sound based on a command from CPU 110.
  • the CPU 110 causes the speaker 107 to output sound based on the sound data.
  • the clock 108 inputs the current date and time to the CPU 110 based on a command from the CPU 110.
  • the CPU 110 executes various types of information processing by executing various programs stored in the memory 101.
  • the processing in the controller 100 is realized by each hardware and software executed by the CPU 110.
  • Such software may be stored in the memory 101 in advance.
  • the software may be stored in a storage medium and distributed as a program product.
  • the software may be provided as a program product that can be downloaded by an information provider connected to the so-called Internet.
  • Such software is read from the storage medium by using a reading device (not shown), or downloaded by using the communication interface 105 and temporarily stored in the memory 101.
  • the CPU 110 stores the software in the form of an executable program in the memory 101 and then executes the program.
  • CD-ROM Compact Disc-Read Only Memory
  • DVD-ROM Digital Versatile Disk-Read Only Memory
  • USB Universal Serial Bus
  • memory card memory card
  • FD Flexible Disk
  • hard disk Magnetic tape, cassette tape, MO (Magnetic Optical Disc), MD (Mini Disc), IC (Integrated Circuit) card (excluding memory card), optical card, mask ROM, EPROM, EEPROM (Electronically Erasable Programmable Read-Only Memory) And the like, for example, a medium for storing the program in a nonvolatile manner.
  • the program here includes not only a program directly executable by the CPU but also a program in a source program format, a compressed program, an encrypted program, and the like.
  • the CPU 110 executes the program to execute the following functional blocks (prediction information parameter generation unit 110F (weather information parameter generation unit 110A, personal information parameter generation unit 110B, A device information parameter generation unit 110C, an equipment information parameter generation unit 110D), a power generation amount prediction unit 110E, a reliability generation unit 110G, and a power consumption amount prediction unit 110H) are realized.
  • prediction information parameter generation unit 110F weather information parameter generation unit 110A, personal information parameter generation unit 110B, A device information parameter generation unit 110C, an equipment information parameter generation unit 110D
  • a power generation amount prediction unit 110E a reliability generation unit 110G
  • a power consumption amount prediction unit 110H a power consumption amount prediction unit 110H
  • the weather information parameter generation unit 110A is realized by the CPU 110, the communication interface 105, the touch panel 106, and the button 104.
  • the weather information parameter generation unit 110A uses the communication interface 105 to receive weather information that is basic prediction information from an external server.
  • the weather information parameter generation unit 110 ⁇ / b> A receives the weather information that is the basic prediction information from the sensors using the communication interface 105.
  • the weather information parameter generation unit 110 ⁇ / b> A receives weather information that is basic prediction information via the touch panel 106 or the button 104.
  • the weather information parameter generation unit 110A accumulates weather information parameters as time series data together with time in a database stored in the memory 101.
  • the personal information parameter generation unit 110B is realized by the CPU 110, the communication interface 105, the touch panel 106, and the button 104. Using the communication interface 105, the personal information parameter generation unit 110B receives personal information that is basic prediction information from an external server. Alternatively, the personal information parameter generation unit 110B receives the personal information that is the basic prediction information from the sensors by using the communication interface 105. Alternatively, the personal information parameter generation unit 110 ⁇ / b> B receives personal information that is basic prediction information via the touch panel 106 or the button 104. The personal information parameter generation unit 110B accumulates personal information parameters as time series data along with the time in a database stored in the memory 101.
  • the device information parameter generation unit 110C is realized by the CPU 110, the communication interface 105, the touch panel 106, and the button 104. Using the communication interface 105, the device information parameter generation unit 110C receives device information that is basic prediction information from an external server. Alternatively, the device information parameter generation unit 110 ⁇ / b> C receives device information that is basic prediction information from sensors using the communication interface 105. Alternatively, the device information parameter generation unit 110 ⁇ / b> C receives device information via the touch panel 106 or the button 104. The device information parameter generation unit 110C accumulates device information parameters as time series data together with time in a database stored in the memory 101.
  • the facility information parameter generation unit 110D is realized by the CPU 110, the communication interface 105, the touch panel 106, and the button 104.
  • the facility information parameter generation unit 110D receives the facility information that is the basic prediction information from an external server using the communication interface 105.
  • the facility information parameter generation unit 110D receives the facility information that is the basic prediction information from the sensors using the communication interface 105.
  • the facility information parameter generation unit 110 ⁇ / b> D receives facility information that is basic prediction information via the touch panel 106 or the button 104.
  • the equipment information parameter generation unit 110D accumulates equipment information parameters as time series data together with time in a database stored in the memory 101.
  • the power generation amount prediction unit 110E is realized by the CPU 110 executing a program. From the database stored in the memory 101, the power generation amount prediction unit 110E generates the power generation amount of the solar cell 200Y corresponding to the latest weather information, the power generation amount of the solar cell 200Y, the power generation amount of the solar cell 200Y, and the solar cell. The amount of power generation 200Y is read.
  • the power generation amount prediction unit 110E is configured to generate the power generation amount corresponding to the latest weather information, the power generation amount corresponding to the latest personal information, and the latest based on the reliability related to weather information, personal information, device information, or facility information. By calculating the power generation amount corresponding to the device information and the power generation amount corresponding to the latest facility information, a predicted value of the power generation amount for a predetermined period ahead of a predetermined time is calculated.
  • the reliability generation unit 110G is realized by the CPU 110 executing a program.
  • the reliability generation unit 110G calculates the reliability related to the weather information based on the time series data of the weather information parameters stored in the memory 101.
  • the reliability generation unit 110G calculates the reliability related to the personal information based on the time series data of the personal information parameters stored in the memory 101.
  • the reliability generation unit 110G calculates the reliability related to the device information based on the time series data of the device information parameter in the database stored in the memory 101.
  • the reliability generation unit 110G calculates the reliability related to the facility information based on the time series data of the facility information parameters in the database stored in the memory 101.
  • FIG. 5 is an image diagram showing how to obtain the reliability according to the present embodiment.
  • the greater the variation in weather information the lower the reliability related to weather information.
  • the greater the fluctuation of personal information the lower the reliability related to personal information.
  • the greater the variation in device information the lower the reliability related to device information.
  • the greater the fluctuation of the facility information the lower the reliability related to the facility information.
  • each degree of reliability related to weather information, personal information, device information, or facility information may be defined to be proportional to the reciprocal of the standard deviation of weather information, personal information, device information, and facility information in a predetermined period. .
  • the power consumption prediction unit 110H is realized by the CPU 110 executing a program. From the database stored in the memory 101, the power consumption amount prediction unit 110H includes the power consumption amounts of a plurality of electrical devices corresponding to the latest weather information and the power consumption amounts of the plurality of electrical devices corresponding to the latest personal information. Then, the power consumption amounts of the plurality of electrical devices corresponding to the latest device information and the values of the power consumption amounts of the plurality of electrical devices corresponding to the latest facility information are read.
  • the power consumption amount prediction unit 110H for each electric device, based on the reliability related to weather information, personal information, device information, or facility information, the power consumption amount corresponding to the latest weather information and the latest personal information Power consumption corresponding to the latest equipment information and power consumption corresponding to the latest equipment information are weighted to give Calculate the predicted value.
  • the power generation amount prediction unit 110E calculates a predicted value of the power consumption amount based only on the power consumption amount corresponding to information whose reliability is equal to or higher than a predetermined value.
  • the power consumption amount prediction unit 110H calculates a predicted value of the power generation amount after a predetermined time from the current time based only on the power generation amount corresponding to the information whose reliability is equal to or higher than the predetermined value.
  • the power generation amount measurement unit (power conditioner 200X) communicates with the CPU 110 and the communication interface 105.
  • CPU110 receives the electric power generation amount of the renewable energy production
  • the device control unit 110J is realized by the CPU 110, the communication interface 105, the touch panel 106, and the button 104.
  • the device control unit 110J includes a predicted value of power consumption for each of the plurality of electrical devices 200, an upper limit value of power consumption for the entire house or office set via the operation unit, and the power generation amount of the solar cell 200Y. Based on the predicted value, the power consumption to be allocated to each electric device 200 is calculated.
  • the device control unit 110J controls the operation of each electrical device 200 using the communication interface 105 based on the power consumption amount allocated to each electrical device 200.
  • the device control unit 110J controls the operation of each electrical device using the communication interface 105 based on a command received from the user via the touch panel 106 or the button 104.
  • FIG. 14 is a block diagram showing a hardware configuration of electric apparatus 200 according to the present embodiment.
  • electric device 200 includes a memory 201, a display 202, a button 204, a communication interface 205, a speaker 207, a sensor 209, and a CPU 210.
  • the memory 201 can be realized in the same manner as the memory 101 that stores the database of the controller 100.
  • the memory 201 stores a control program executed by the CPU 210, a power consumption amount of the electric device 200, a command input to the electric device 200, an operation state of the electric device 200, and the like.
  • the display 202 is controlled by the CPU 210. More specifically, the display 202 displays a still image or a moving image based on data from a TV tuner or VRAM (Video RAM) (not shown).
  • VRAM Video RAM
  • the button 204 is disposed on the surface of the electric device 200.
  • the electric device 200 may include a plurality of buttons 204 such as a determination key, a direction key, and a numeric keypad.
  • the button 204 receives a command from the user and inputs the command to the CPU 210.
  • the communication interface 205 transmits / receives data to / from the controller 100 via the network by being controlled by the CPU 210.
  • the communication interface 205 is connected to the controller 100 by using a wireless LAN, ZigBee (registered trademark), Bluetooth (registered trademark), a wired LAN (Local Area Network), or a PLC (Power Line Communications). Send and receive data.
  • Speaker 207 outputs sound based on a command from CPU 210.
  • the CPU 210 causes the speaker 207 to output sound based on the sound data.
  • the sensor 209 measures the power consumption of the electric device 200 and transmits the power consumption to the CPU 210.
  • the CPU 210 executes various types of information processing by executing various programs stored in the memory 201.
  • the processing in the electric device 200 is realized by each hardware and software executed by the CPU 210.
  • Such software may be stored in the memory 201 in advance.
  • the software may be stored in a storage medium and distributed as a program product.
  • the software may be provided as a program product that can be downloaded by an information provider connected to the so-called Internet.
  • Such software is read from the storage medium by using a reading device (not shown), or downloaded by using the communication interface 205 and temporarily stored in the memory 201.
  • the CPU 210 stores the software in the form of an executable program in the memory 201 and then executes the program. Note that the storage medium and the program can be realized in the same manner as the recording medium and program according to the controller 100.
  • the CPU 210 receives a control command from the controller 100 via the communication interface 205.
  • CPU110 controls each part of the electric equipment 200 based on a control command.
  • the CPU 210 of the heat pump water heater 200A receives a control command from the controller 100 and increases the target temperature of the tank or increases the circulation speed.
  • the present invention can also be applied to a case where it is achieved by supplying a program to the controller 100, an electric device, another communication device, or the like. Then, a storage medium storing a program represented by software for achieving the present invention is supplied to the system or apparatus, and the computer (or CPU or MPU) of the system or apparatus stores the program code stored in the storage medium It is possible to enjoy the effects of the present invention also by reading and executing.
  • the program code itself read from the storage medium realizes the functions of the above-described embodiment, and the storage medium storing the program code constitutes the present invention.
  • the function expansion is performed based on the instruction of the program code. It goes without saying that the CPU or the like provided in the board or the function expansion unit performs part or all of the actual processing and the functions of the above-described embodiments are realized by the processing.
  • 1 network system 100 controller, 101 memory, 102, 202 display, 103 tablet, 104, 204 button, 105, 205 communication interface, 106 touch panel, 107, 207 speaker, 108 clock, 110 CPU, 110A weather information parameter generator, 110B personal information parameter generation unit, 110C device information parameter generation unit, 110D facility information parameter generation unit, 110E power generation amount prediction unit, 110F prediction information parameter generation unit, 110G reliability generation unit, 110H power consumption prediction unit, 110J device control Part, 200 electrical equipment, 200A heat pump water heater, 200B air conditioner, 200C refrigerator, 200D lighting, 200E LCD TV, 200G Human sensor, 200H temperature / humidity sensor, 200I power sensor, 200J anemometer, 200K anemometer, 200L pyranometer, 200R smart meter, 200S sensing device, 200T camera device, 200U power consumption measuring unit, 200X power conditioner, 200Y Solar cell, 200Z storage battery, 201 memory, 209 sensor, 210 CPU (processor), 400Y renewable energy generator.

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Abstract

La présente invention a pour objet un contrôleur capable de contrôler de façon plus appropriée un dispositif de consommation d'énergie selon un environnement externe. Le contrôleur (100) est destiné à commander une pluralité de dispositifs électriques connectés par l'intermédiaire d'un réseau. Le contrôleur (100) comprend : une mémoire (101) qui stocke les consommations d'énergie d'une pluralité de dispositifs électriques, lesdites consommations d'énergie étant associées à chaque élément d'une pluralité d'éléments d'informations ; une interface de communication (105) destinée à recevoir de façon intermittente des informations depuis l'extérieur ; et un processeur (110) destiné à déterminer la fiabilité des informations, calculer une consommation d'énergie estimée sur la base de la fiabilité, et commander la pluralité de dispositifs électriques en fonction de la consommation d'énergie estimée.
PCT/JP2012/059185 2011-04-04 2012-04-04 Contrôleur et procédé de commande WO2012137814A1 (fr)

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